There is a plethora of content available today and it is growing by leaps and bounds. There is a need to organize this content into categories and ensure that it shows up if it is searched for. This is especially important for e-commerce businesses and retailers who have catalogs of products. Here is when search engines play a critical role — be it the Google/Bing engines or the on-site product search engines that help users find what they desire.

It is important for retailers and e-commerce businesses to understand and analyze the needs and behavior of their target customers. Listening to what they express online via social media or forums becomes imperative for these businesses to provide a better customer experience. This also helps them to understand what kind of language the user may use to buy a specific item.

While this may be an easy task for humans, it is time-consuming. Here is where AI and Machine Learning fits perfectly. Using Natural Language Processing (NLP), machines can easily pick on what words or phrases humans would naturally use while looking for a particular item.

Natural Language Processing is the ability of a computer program to understand human language as it is spoken. Human speech is often ambiguous and the linguistic structure can depend on various complex variables, including the regional dialects and social context including colloquial terminologies.

Using a search engine is interacting with a system, and utilizing NLP helps customize the search for the user. Using NLP helps the system understand what kind of language was used and how the sentence was structured. Using these points, the system derives what the user is actually searching for, and provide results accordingly. Detecting patterns and creating links between the messaging is what it does best, and with Natural Language Processing, it is powered to derive meanings from unstructured text.

For instance, a search query for “sleeveless men’s shirts” would involve understanding the context of the words, and without NLP, search engines would unable to process the link between sleeveless and shirts and the results would end up looking like this:

Here, the word “shirt” has not been taken into account, and the results have shown only sleeveless “t-shirts” or vests instead of the intended search, “sleeveless shirt.”

Why Do Users Search for “Top Budget-Friendly Phones From 2018” on a Search Engine yet Not on E-Commerce Websites Directly?

An intent for a search would be to find discussions and do some research in the user’s purchase process. And while the word “top” is subjective, content creators and SEO agencies (providing product lists) usually pick words such as “top” or “best” in their communication.

Whereas, in an e-commerce store, users understand that using words like “top” or “best” is subjective. There is no rule that can translate “budget-friendly” being “less than $200” since it depends on the type of product as well as the perception of “budget-friendly.” The advantage of keyword heavy communication is that the format of communication is standardized, which works on most e-commerce sites.

What's Plaguing Natural Language Processing Today?

The performance of the NLP model depends directly on the quantity and quality of the data that it is fed, as s the case with every ML model. Retailers and e-commerce businesses need to consider the problem with synonyms and slangs which works differently in different regions. Lexical databases such as WordNet can come in handy, but they are limited to English and therefore it may not work for international stores, catering to customers from different cultures and languages.

There is a high possibility of a discrepancy between what a customer calls a product and how the metadata describes the product. The words that customers use to describe the desired product often describes another product rather than the one they want.

Will NLP Be the Future of E-Commerce Product Search?

Successful integration of NLP into online product search is still challenging. In a typical retail eCommerce application, it would involve getting an algorithm to gather data about all the products being sold and put in a structure and normalize it. It would then find all linguistic attributes that would be used to describe each product. The challenge here is that leveraging NLP technologies put the burden on search engines and not on the consumer to make the experience natural.

Online product search will evolve in a manner in which the context understanding will be integrated with the search engine allowing humans to have conversations with them in a natural environment. For example, Customers searching for fashion products have a different way of phrasing requests as opposed to customers searching for home furnishing products. NLP platforms of the future would be able to contextually understand these variations.

As NLP gains momentum, the growth would give an increase in its capability to provide better customer experiences. NLP may very well be the future.